A framework for regional smart energy planning using volunteered geographic information

Abstract. This study presents a framework for regional smart energy\nplanning for the optimal location and sizing of small hybrid systems. By\nusing an optimization model – in combination with weather data – various\nlocal energy systems are simulated using the Calliope and PyPSA energy\nsystem simulation tools. The optimization and simulation models are fed with\nGIS data from different volunteered geographic information projects,\nincluding OpenStreetMap. These allow automatic allocation of specific demand\nprofiles to diverse OpenStreetMap building categories. Moreover, based on\nthe characteristics of the OpenStreetMap data, a set of possible distributed\nenergy resources, including renewables and fossil-fueled generators, is\ndefined for each building category. The optimization model can be applied\nfor a set of scenarios based on different assumptions on electricity prices\nand technologies. Moreover, to assess the impact of the scenarios on the\ncurrent distribution infrastructure, a simulation model of the low- and\nmedium-voltage network is conducted. Finally, to facilitate their\ndissemination, the results of the simulation are stored in a PostgreSQL\ndatabase, before they are delivered by a RESTful Laravel Server and\ndisplayed in an angular web application.